An Overview of Problems and Approaches in Machine Intelligence
Résumé
Robotics is an interdisciplinary research field leveraging on control theory, mechanical engineering, electronic engineering and computer science. It aims at designing machines able to perceive, move around and interact with their environment in order to perform useful tasks. Artificial Intelligence (AI) is an area of computer science, overlapping with but significantly distinct from robotics. Its purpose is to understand intelligence, through effective computational models, design and experiment with systems which implement these models. There is a significant convergence between Robotics and AI. Their intersection, qualified here as Machine Intelligence, is critical for both areas. Robots implement the so-called " perception-decision-action " loop; the intelligence or decision making part is central in that loop for tackling more variable and complex environments and tasks. On the other hand, AI is moving from abstract intelligence, such as in playing chess, to addressing embodied intelligence. This paper introduces the reader to some of the research issues and approaches in Machine Intelligence. It surveys the state of the art in key issues such as planning and acting deliberately on the basis of tasks and world models, learning these models, and organizing the sensory-motor and cognitive functions of a robot into resilient and scalable architectures.
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